The healthcare landscape is a dynamic ecosystem, constantly growing with new drugs, cosmetics, nutraceuticals, and medical devices. The global healthcare market size including drugs, cosmetics, nutraceuticals, and devices is anticipated to be in the region of $10 trillion by 2025 with a broad average CAGR of 5%. This rapid growth and innovation, while exciting, necessitates robust safety measures. Multivigilance, the comprehensive, end-to-end monitoring of a product’s safety profile, plays a critical role in safeguarding patient well-being.
This blog dips into the challenges currently faced by the industry in ensuring product safety. We will touch upon recent regulatory developments aimed at strengthening multivigilance, and propose how advancements in Artificial Intelligence (AI) can offer powerful solutions.
Industry Context for Safety Vigilance
Key aspects of the evolving Life Sciences market landscape include:
- ➣ Rapidly growing, diversified conglomerates that develop and market a broad range of products, including drugs, cosmetics, vaccines and devices.
- ➣ Keeping pace with evolving regulations across the different sectors, countries/regions and product categories is a constant challenge.
- ➣ Safety data often resides in many silos – healthcare records, spontaneous reports, clinical trial data, social platforms and scientific literature. Integrating and analysing this multi-modal, fragmented data across varied contexts poses a challenge from an efficiency perspective.
- ➣ Identifying early warning signs (signals) of potential safety issues requires efficient analysis of vast amounts of complex data.
- ➣ Resource and budget constraints can hinder thorough safety monitoring, particularly for small to medium size businesses.
New Regulatory Guidance
New regulatory guidance, such as MoCRA (Modernization of Cosmetics Regulation Act), raises the bar for cosmetic safety in the US (and for those exporting products there) by requiring more rigorous testing methods and more frequent reporting of adverse events. Regulatory bodies like the FDA (US Food and Drug Administration), EMA and MHRA are increasingly scrutinizing nutraceuticals and devices, demanding enhanced safety monitoring protocols.
The Role of AI in Multivigilance
AI can potentially play a crucial role in multivigilance by significantly enhancing the monitoring, intake and analysis of adverse events. AI models can efficiently analyse vast amounts of data from various sources to identify potential safety concerns in real-time. By automating processes and offering predictive analytics, AI empowers healthcare professionals and regulatory authorities to swiftly respond to emerging risks, improve patient safety, and ensure the efficacy of medical interventions. Additionally, AI-driven multivigilance systems enable proactive measures for risk mitigation and support evidence-based decision-making in healthcare.
Introducing Datafoundry’s Safety 4.0 Platform
DF mSafety AI is a modern, cloud-based multivigilance SaaS solution for end-to-end Safety Case Management with AI automation from Case Intake, through QC, Medical Review, Reports and Submissions. The solution enables users to switch between Pharmacovigilance, Cosmetovigilance and Materiovigilance flavours seamlessly. Pre-trained and validated ML models for Seriousness Prediction, MedDRA autocoding, Causality assessment and Narrative generation help improve efficiencies.The automation can deliver up to 40% effort and time savings, delivering cost-effective compliance in a secure and scalable manner.
DF mSignal AI integrates regulatory agency safety case data with company safety database and harnesses the power of AI-driven analytics to detect and evaluate safety signals across pharmaceuticals, cosmetics, medical devices, and other cross-sector products. With features such as Active Surveillance, Clinical Risk Flags, rich and interactive analytics and AI predictions for signal detection, DF mSignal AI enables early detection of emerging risks, facilitating timely intervention and risk management strategies. The solution enables tracking of signal and risk management actions to closure.
DF Literature Monitor elevates Literature Surveillance for Safety and Medical Affairs use cases to the next level, with an out-of-the-box integration of 25+ open access literature databases and an AI-powered semantic search. For Safety Vigilance, articles with Potential AE are identified by the NLP algorithm, and users are provided with auto-filled safety case data, thus reducing manual and repetitive effort by 60-70%. The solution also leverages Machine Translation models to enable management of local literature database search. The E2B export of valid ICSRs enable easy integration with Safety databases for medical review and submission.
Conclusion
In conclusion, the burgeoning landscape of multivigilance presents both large challenges and opportunities in ensuring the safety and efficacy of diverse product ranges across various sectors. As data volumes continue to escalate, organizations equipped with advanced multivigilance solutions powered by AI stand poised for success in this dynamic environment. By leveraging the capabilities of AI-driven multivigilance systems, businesses can effectively navigate through the complexities of regulatory compliance, swiftly detect emerging risks, and uphold consumer safety standards. Datafoundry offers tailored solutions and expertise in multivigilance, empowering organizations to make informed decisions, mitigate risks, and drive innovation while ensuring regulatory compliance. Embracing these solutions enables businesses to thrive amidst the evolving landscape of multivigilance, maximizing value and enhancing consumer trust in an increasingly digitalized world.
Please watch this space for more articles that will delve into the aspect of Multivigilance using the available and evolving AI/ML technologies. If you want a specific use case to be discussed, please feel free to comment below or write to: chris.s@datafoundry.ai